ArticleJournal of imaging2023
VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- A Systematic Review of Deep Learning Approaches for Hepatopancreatic Tumor Segmentation.Journal of imaging · 2026Review
- Convpaint-Interactive pixel classification using pretrained neural networks.Cell reports methods · 2026Article
- Systematic investigation of pre-processing and feature extraction techniques in medical image analysis.Discover artificial intelligence · 2026Article
- A TabPFN-based prediction system for refractive error and dry eye comorbidity: a retrospective study using large-scale real-world data.Frontiers in cell and developmental biology · 2026Article
- DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging.Abdominal radiology (New York) · 2025Article
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective Visual Evaluation.Diagnostics (Basel, Switzerland) · 2025Article
- Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.Cancer science · 2024Observational
- CTDUNet: A Multimodal CNN-Transformer Dual U-Shaped Network with Coordinate Space Attention forPlants (Basel, Switzerland) · 2024Article
- Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence.Cancers · 2024Review
- Automating Linear and Angular Measurements for the Hip and Knee After Computed Tomography: Validation of a Three-Stage Deep Learning and Computer Vision-Based Pipeline for Pathoanatomic Assessment.Arthroplasty today · 2024Article
- Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation.Journal of imaging · 2024Article
- Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction.PloS one · 2024Article
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2 authors.
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Abstract
The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly improved by an early and accurate diagnosis. Several studies have created automated methods to forecast PDAC development utilising various medical imaging modalities. These papers give a general overview of the classification, segmentation, or grading of many cancer types utilising conventional machine learning techniques and hand-engineered characteristics, including pancreatic cancer. This study uses cutting-edge deep learning techniques to identify PDAC utilising computerised tomography (CT) medical imaging modalities. This work suggests that the hybrid model VGG16-XGBoost (VGG16-backbone feature extractor and Extreme Gradient Boosting-classifier) for PDAC images. According to studies, the proposed hybrid model performs better, obtaining an accuracy of 0.97 and a weighted F1 score of 0.97 for the dataset under study. The experimental validation of the VGG16-XGBoost model uses the Cancer Imaging Archive (TCIA) public access dataset, which has pancreas CT images. The results of this study can be extremely helpful for PDAC diagnosis from computerised tomography (CT) pancreas images, categorising them into five different tumours (T), node (N), and metastases (M) (TNM) staging system class labels, which are T0, T1, T2, T3, and T4.
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